Forming Optimal Projection Images from Intra-Retinal Layers Using Curvelet-Based Image Fusion Method

Jalil Jalili, Hossein Rabbani, Alireza Mehri Dehnavi, Raheleh Kafieh, Mohammadreza Akhlaghi

DOI: 10.4103/jmss.JMSS_43_19

Abstract


Background: Image fusion is the process of combining the information of several input images into one image. Projection images obtained from three-dimensional (3D) optical coherence tomography (OCT) can show inlier retinal pathology and abnormalities that are not visible in conventional fundus images. In recent years, the projection image is often made by an average on all retina that causes to lose many intraretinal details. Methods: In this study, we focus on the formation of optimum projection images from retinal layers using Curvelet-based image fusion. The latter consists of three main steps. In the earlier studies, macular spectral 3D data using diffusion map-based OCT were segmented into 12 different boundaries identifying 11 retinal layers in three dimensions. In the second step, projection images are attained using conducting some statistical methods on the space between each pair of boundaries. In the next step, retinal layers are merged using Curvelet transform to make the final projection images. Results: These images contain integrated retinal depth information as well as an ideal opportunity to better extract retinal features such as vessels and the macula region. Finally, qualitative and quantitative evaluations show the superiority of this method to the average-based and wavelet-based fusion methods. Overall, our method obtains the best results for image fusion in all terms such as entropy (6.7744) and AG (9.5491). Conclusion: Creating an image with more and detailed information made by the Curvelet-based image fusion has significantly higher contrast. There are also many thin veins in Curvelet-based fused image, which are absent in average-based and wavelet-based fused images.

Keywords


Curvelet transform, image fusion, optical coherence tomography, projection image, retina

Full Text:

PDF

References


Brezinski ME. Optical Coherence Tomography: Principles and Applications. Elsevier; 2006. Back to cited text no. 1

Regatieri CV, Branchini L, Carmody J, Fujimoto JG, Duker JS. Choroidal thickness in patients with diabetic retinopathy analyzed by spectral-domain optical coherence tomography. Retina 2012;32:563-8. Back to cited text no. 2

Wilde C, Patel M, Lakshmanan A, Amankwah R, Dhar-Munshi S, Amoaku W, et al. The diagnostic accuracy of spectral-domain optical coherence tomography for neovascular age-related macular degeneration: A comparison with fundus fluorescein angiography. Eye (Lond) 2015;29:602-9. Back to cited text no. 3

Fu D, Tong H, Zheng S, Luo L, Gao F, Minar J. Retinal status analysis method based on feature extraction and quantitative grading in OCT images. Biomed Eng Online 2016;15:87. Back to cited text no. 4

Novosel J, Vermeer KA, de Jong JH, Ziyuan Wang, van Vliet LJ. Joint segmentation of retinal layers and focal lesions in 3-D OCT data of topologically disrupted retinas. IEEE Trans Med Imaging 2017;36:1276-86. Back to cited text no. 5

Kafieh R, Rabbani H, Kermani S. A review of algorithms for segmentation of optical coherence tomography from retina. J Med Signals Sens 2013;3:45-60. Back to cited text no. 6

[PUBMED] [Full text]

Garvin MK, Abràmoff MD, Lee K, Niemeijer M, Sonka M, Kwon YH. 2-D pattern of nerve fiber bundles in glaucoma emerging from spectral-domain optical coherence tomography. Invest Ophthalmol Vis Sci 2012;53:483-9. Back to cited text no. 7

Golabbakhsh M, Rabbani H. Vessel-based registration of fundus and optical coherence tomography projection images of retina using a quadratic registration model. IET Image Process 2013;7:768-76. Back to cited text no. 8

Gorczynska I, Srinivasan VJ, Vuong LN, Chen RW, Liu JJ, Reichel E, et al. Projection OCT fundus imaging for visualising outer retinal pathology in non-exudative age-related macular degeneration. Br J Ophthalmol 2009;93:603-9. Back to cited text no. 9

Heiferman M, Simonett J, Fawzi A. En face OCT imaging in retinal disorders. Retin Physician 2015;12:45. Back to cited text no. 10

Sayanagi K, Gomi F, Akiba M, Sawa M, Hara C, Nishida K. En-face high-penetration optical coherence tomography imaging in polypoidal choroidal vasculopathy. Br J Ophthalmol 2015;99:29-35. Back to cited text no. 11

Stopa M, Bower BA, Davies E, Izatt JA, Toth CA. Correlation of pathologic features in spectral domain optical coherence tomography with conventional retinal studies. Retina 2008;28:298-308. Back to cited text no. 12

Wanek J, Zelkha R, Lim JI, Shahidi M. Feasibility of a method for en face imaging of photoreceptor cell integrity. Am J Ophthalmol 2011;152:807-140. Back to cited text no. 13

Murakami T, Nishijima K, Akagi T, Uji A, Horii T, Ueda-Arakawa N, et al. Optical coherence tomographic reflectivity of photoreceptors beneath cystoid spaces in diabetic macular edema. Invest Ophthalmol Vis Sci 2012;53:1506-11. Back to cited text no. 14

Alasil T, Ferrara D, Adhi M, Brewer E, Kraus MF, Baumal CR, et al. En face imaging of the choroid in polypoidal choroidal vasculopathy using swept-source optical coherence tomography. Am J Ophthalmol 2015;159:634-43. Back to cited text no. 15

Fard MA, Jalili J, Sahraiyan A, Khojasteh H, Hejazi M, Ritch R, et al. Optical Coherence Tomography Angiography in Optic Disc Swelling. Am J Ophthalmol 2018;191:116-23. Back to cited text no. 16

Fard MA, Sahraiyan A, Jalili J, Hejazi M, Suwan Y, Ritch R, et al. Optical coherence tomography angiography in papilledema compared with pseudopapilledema. Invest Ophthalmol Vis Sci 2019;60:168-75. Back to cited text no. 17

Hong Y, Makita S, Yamanari M, Miura M, Kim S, Yatagai T, et al. Three-dimensional visualization of choroidal vessels by using standard and ultra-high resolution scattering optical coherence angiography. Opt Express 2007;15:7538-50. Back to cited text no. 18

Niemeijer M, Garvin MK, van Ginneken B, Sonka M, Abramoff MD, editors. Vessel Segmentation In 3D Spectral Oct Scans of the Retina. Medical Imaging 2008: Image Processing. San Diego, California, United States: International Society for Optics and Photonics; 2008. Back to cited text no. 19

Jalili J, Rabbani H, Akhlaghi M, Kafieh R, Mehridehnavi A, editors. Forming Projection Images from Each Layer of Retina Using Diffusion May Based Oct Segmentation. 2012 11th International Conference on Information Science. Montreal, Canada: Signal Processing and their Applications; 2012. Back to cited text no. 20

Jalili J, Rabbani H, Mehri-Dehnavi A, Akhlaghi M. Formation and fusion of projection images from 11 layers of retina using statistical indicators to obtain an image with appropriate contrast from the retinal depth. J Isfahan Med Sch 2013;31:255. Back to cited text no. 21

Zhang Y. Understanding image fusion. Photogramm Eng Remote Sens 2004;70:657-61. Back to cited text no. 22

Sahu DK, Parsai M. Different image fusion techniques–a critical review. Int J Mod Eng Res 2012;2:4298-301. Back to cited text no. 23

Thakare VV, Katiyar P. Review on various image fusion techniques. J Multimed Technol Recent Adv 2018;5:14-7. Back to cited text no. 24

Ali F, El-Dokany I, Saad A, Abd El-Samie FE. Curvelet fusion of MR and CT images. Prog Electromagn Res 2008;3:215-24. Back to cited text no. 25

Yelampalli PK, Nayak J, Gaidhane VH. Daubechies wavelet-based local feature descriptor for multimodal medical image registration. IET Image Process 2018;12:1692-702. Back to cited text no. 26

Miri MS, Mahloojifar A. Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. IEEE Trans Biomed Eng 2011;58:1183-92. Back to cited text no. 27

Starck JL, Murtagh F, Candès EJ, Donoho DL. Gray and color image contrast enhancement by the curvelet transform. IEEE Trans Image Process 2003;12:706-17. Back to cited text no. 28

Ma J, Plonka G. The curvelet transform. IEEE Signal Process Mag 2010;27:118-33. Back to cited text no. 29

Arif M, Wang G. Fast curvelet transform through genetic algorithm for multimodal medical image fusion. Soft Comput 2019:1-22. Back to cited text no. 30

Nencini F, Garzelli A, Baronti S, Alparone L. Remote sensing image fusion using the curvelet transform. Inf Fusion 2007;8:143-56. Back to cited text no. 31

Yang J, Zhao ZM. Multi-focus image fusion method based on curvelet transform. Opto Electron Eng 2007;6:67-71. Back to cited text no. 32

Yang Y, Tong S, Huang S, Lin P, Fang Y. A hybrid method for multi-focus image fusion based on fast discrete curvelet transform. IEEE Access 2017;5:14898-913. Back to cited text no. 33

Kafieh R, Rabbani H, Abramoff MD, Sonka M. Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map. Med Image Anal 2013;17:907-28. Back to cited text no. 34

Kafieh R, Rabbani H, Hajizadeh F, Abramoff MD, Sonka M. Thickness mapping of eleven retinal layers segmented using the diffusion maps method in normal eyes. J Ophthalmol 2015;2015:259123. Back to cited text no. 35

Bhateja V, Krishn A, Sahu A, editors. Medical Image Fusion in Curvelet Domain Employing PCA and Maximum Selection Rule. Proceedings of the Second International Conference on Computer and Communication Technologies. Udaipur India; Publisher: Association of computing machinery, New York, US, Springer; 2016. Back to cited text no. 36

Yang G, Li M, Chen L, Yu J. The nonsubsampled contourlet transform based statistical medical image fusion using generalized Gaussian density. Comput Math Methods Med 2015;2015:262819. Back to cited text no. 37

Lahmiri S. Wavelet low-and high-frequency components as features for predicting stock prices with backpropagation neural networks. J King Saud Univ Comput Inf Sci 2014;26:218-27. Back to cited text no. 38

Kafieh R, Danesh H, Rabbani H, Abramoff M, Sonka M, editors. Vessel Segmentation in Images of Optical Coherence Tomography Using Shadow Information and Thickening of Retinal Nerve Fiber Layer. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada. 2013. p. 1075-9. Back to cited text no. 39

Hood DC, Fortune B, Arthur SN, Xing D, Salant JA, Ritch R, et al. Blood vessel contributions to retinal nerve fiber layer thickness profiles measured with optical coherence tomography. J Glaucoma 2008;17:519-28. Back to cited text no. 40

Yang S, Wang M, Jiao L, Wu R, Wang Z. Image fusion based on a new contourlet packet. Inf Fusion 2010;11:78-84. Back to cited text no. 41

Alipour SH, Houshyari M, Mostaar A. A novel algorithm for PET and MRI fusion based on digital curvelet transform via extracting lesions on both images. Electron Physician 2017;9:4872-9. Back to cited text no. 42

Singh R, Khare A. Multiscale medical image fusion in wavelet domain. ScientificWorldJournal 2013;2013:Article ID: 521034. Back to cited text no. 43


Refbacks

  • There are currently no refbacks.


 

  https://e-rasaneh.ir/Certificate/22728

https://e-rasaneh.ir/

ISSN : 2228-7477